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arxiv logo>cs> arXiv:2411.14652
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Computer Science > Computers and Society

arXiv:2411.14652 (cs)
[Submitted on 22 Nov 2024]

Title:Social Media Algorithms Can Shape Affective Polarization via Exposure to Antidemocratic Attitudes and Partisan Animosity

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Abstract:There is widespread concern about the negative impacts of social media feed ranking algorithms on political polarization. Leveraging advancements in large language models (LLMs), we develop an approach to re-rank feeds in real-time to test the effects of content that is likely to polarize: expressions of antidemocratic attitudes and partisan animosity (AAPA). In a preregistered 10-day field experiment on X/Twitter with 1,256 consented participants, we increase or decrease participants' exposure to AAPA in their algorithmically curated feeds. We observe more positive outparty feelings when AAPA exposure is decreased and more negative outparty feelings when AAPA exposure is increased. Exposure to AAPA content also results in an immediate increase in negative emotions, such as sadness and anger. The interventions do not significantly impact traditional engagement metrics such as re-post and favorite rates. These findings highlight a potential pathway for developing feed algorithms that mitigate affective polarization by addressing content that undermines the shared values required for a healthy democracy.
Subjects:Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Social and Information Networks (cs.SI)
Cite as:arXiv:2411.14652 [cs.CY]
 (orarXiv:2411.14652v1 [cs.CY] for this version)
 https://doi.org/10.48550/arXiv.2411.14652
arXiv-issued DOI via DataCite

Submission history

From: Tiziano Piccardi [view email]
[v1] Fri, 22 Nov 2024 00:55:07 UTC (3,286 KB)
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